Nonlinear, reduced order, distributed state estimation in microgrids
Shivam Saxena, Amir Asif, Hany Essa Zidan Farag
Abstract
Recent developments in microgrids place strict constraints on the underlying state estimation technology, including the need for a dynamic and distributed approach. Since the problem is reminiscent of classical information fusion [2], the paper explores the application of a fusion-based reduced order, distributed unscented particle filter (FR/DUPF) for dynamic state estimation in microgrids. By partitioning the nonlinear microgrid into a network of n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub</sub> localized and dynamically coupled systems, the FR/DUPF provides computational savings of a factor of n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub</sub> over its centralized version. Monte Carlo simulations verify its accuracy by confirming that estimates from the FR/DUPF and centralized filter evolve close to the ground truth.
BibTeX
@inproceedings{icassp2015_nonlinearreduced,
title = {Nonlinear, reduced order, distributed state estimation in microgrids},
author = {Shivam Saxena and Amir Asif and Hany Essa Zidan Farag},
booktitle = {ICASSP 2015},
year = {2015}
}